Online monitoring system, method and equipment for oil and gas recovery of gas station and medium

Abstract

The invention particularly relates to a gas station oil gas recovery online monitoring system, method and device and a medium, the monitoring system comprises a sensor network, an edge controller and a cloud computing monitoring platform, and the sensor network is used for collecting working parameters at key nodes in the oil gas recovery process. And the edge controller is used for performing preliminary data processing based on the working parameters to obtain oil gas recovery performance parameters and refueling equipment early warning information. And furthermore, the cloud computing monitoring platform is used for acquiring the oil gas recovery performance parameter sent by each edge controller in the gas station, and performing multi-dimensional data analysis based on each oil gas recovery performance parameter to obtain an oil gas recovery multi-dimensional monitoring result. By executing multi-dimensional data analysis, abnormal conditions in the oil and gas recovery process can be found and fed back in time, efficient oil and gas recovery work is ensured, meanwhile, potential fault points can be recognized in advance, the downtime and maintenance cost caused by faults are reduced, and the stability and reliability of the oil and gas recovery process are improved.

Description

Technical Field

[0001] The present application relates to the technical field of gas station safety monitoring, and in particular to a gas station oil and gas recovery online monitoring system, method, equipment and medium. Background Art

[0002] During the oil storage and refueling process at gas stations, a large amount of volatile organic compounds will be generated. If these oil and gas are directly discharged into the atmosphere without treatment, it will not only cause serious air pollution, but also waste precious energy resources and even increase the risk of fire and explosion. In order to deal with this problem, it is particularly important for gas stations to adopt oil and gas recovery technology during the oil storage and refueling process.

[0003] However, although the oil vapor recovery system in gas stations has been widely used, there are still some stability and reliability issues in actual operation. For example, due to equipment aging, improper operation or untimely maintenance, the performance of the oil vapor recovery system may decline, resulting in reduced oil vapor recovery efficiency.

[0004] Therefore, how to provide an oil and gas recovery online monitoring system to monitor the various performance indicators of the oil and gas recovery system in real time to improve the stability and reliability of the oil and gas recovery process is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] The purpose of this application is to provide an online monitoring system, method, equipment and medium for oil and gas recovery at a gas station. An online monitoring system, method, equipment and medium for oil and gas recovery at a gas station are used to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In the first aspect, the present application provides an online monitoring system for oil and gas recovery at a gas station, which adopts the following technical solution: An online monitoring system for oil and gas recovery at a gas station includes: a sensor network, an edge controller, and a cloud computing monitoring platform, wherein: A sensor network for collecting working parameters at key nodes in the oil and gas recovery process, wherein the sensor network is constructed by the joint work of different types of sensors deployed at key nodes, and the key nodes are equipment parts that affect the safety of oil and gas recovery; The edge controller is used to obtain the working parameters corresponding to the refueling equipment from the sensor network in real time, perform preliminary data processing based on the working parameters, and obtain oil and gas recovery performance parameters and refueling equipment warning information, wherein a plurality of the refueling equipment are provided in the gas station, and each of the refueling equipment is provided with a corresponding edge controller; The cloud computing monitoring platform is used to obtain the oil and gas recovery performance parameters sent by each edge controller in the gas station, perform multi-dimensional data analysis based on each oil and gas recovery performance parameter, and obtain multi-dimensional monitoring results of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working status optimization analysis.

[0007] By adopting the above technical solution, the online monitoring system for oil and gas recovery in gas stations includes: a sensor network, an edge controller and a cloud computing monitoring platform. The sensor network is used to collect the working parameters at each key node in the oil and gas recovery process. The edge controller is used to obtain the working parameters corresponding to the refueling equipment from the sensor network in real time, and perform preliminary data processing based on the working parameters to obtain the oil and gas recovery performance parameters and the early warning information of the refueling equipment. The data calculation work is distributed on the edge controller and the cloud computing monitoring platform. The edge controller can perform preliminary data processing operations so that it can respond quickly when there is an abnormality in the oil and gas recovery process, reduce the computing burden of the cloud computing monitoring platform, and reduce the transmission delay of the processing data. Furthermore, the cloud computing monitoring platform is used to obtain the oil and gas recovery performance parameters sent by each edge controller in the gas station, and perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain the multi-dimensional monitoring results of oil and gas recovery. Performing multi-dimensional data analysis can timely discover and feedback abnormal situations in the oil and gas recovery process, ensure the efficient progress of the oil and gas recovery work, and at the same time, it can identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process.

[0008] In a preferred example, the present application may be further configured as follows: when the cloud computing monitoring platform performs the multi-dimensional data analysis based on each of the oil and gas recovery performance parameters to obtain the oil and gas recovery multi-dimensional monitoring results, it is used to: Performing data classification based on each of the oil and gas recovery performance parameters to obtain a multi-dimensional performance parameter set; Performing a working state analysis based on each of the performance parameter sets to determine a working state analysis result, wherein the working state analysis result includes: a normal working state and an abnormal working state; When the working state analysis result is that the working state is normal, a potential fault prediction is performed based on each of the performance parameter sets to determine a potential fault prediction result, and a working optimization analysis is performed based on each of the performance parameter sets to determine a working optimization analysis result; The multi-dimensional monitoring result of oil and gas recovery is obtained by combining the working status analysis result, the potential fault prediction result and the working optimization analysis result.

[0009] In a preferred example, the present application may be further configured as follows: the cloud computing monitoring platform is also used to: When the working state analysis result is that the working state is abnormal, abnormal data extraction is performed based on each of the performance parameter sets to obtain abnormal performance parameters; An abnormal performance analysis is performed based on the abnormal performance parameters to obtain abnormal performance characteristics, and based on the relationship between the abnormal performance characteristics and operation and maintenance processing, a fault suggestion processing plan corresponding to the abnormal performance characteristics is determined, wherein the fault suggestion processing plan provides fault processing suggestions for gas station managers to facilitate rapid resolution of abnormal working conditions.

[0010] In a preferred example, the present application can be further configured as follows: A holographic projection display platform is used to obtain gas station deployment information and sensor network deployment information, and to construct a three-dimensional model based on the gas station deployment information and the sensor network deployment information to obtain an oil and gas recovery three-dimensional model; Based on the oil and gas recovery performance parameters corresponding to each refueling equipment and the corresponding elements in the oil and gas recovery three-dimensional model, the oil and gas recovery three-dimensional model after element binding is visualized and rendered using holographic projection technology to obtain a virtual three-dimensional image reflecting the real-time operating status of oil and gas recovery.

[0011] In a preferred example, the present application may be further configured as follows: the cloud computing monitoring platform is also used to: Acquire the refueling equipment warning information sent by each edge controller in real time, and count the number of warnings based on the refueling equipment warning information to determine the number of warnings; Acquire alarm monitoring requirements, perform alarm analysis based on the number of warnings, the refueling equipment warning information and the alarm monitoring requirements, and determine alarm analysis results and alarm control methods; When the alarm analysis result is to execute an alarm, the refueling equipment corresponding to the edge controller is controlled according to the alarm control method, and the alarm device is controlled to alarm.

[0012] In a preferred example, the present application can be further configured as follows: A sensor network building module, used for screening nodes based on the oil and gas recovery process and determining multiple key nodes; Performing sensor analysis based on each of the key nodes to determine sensor information corresponding to each of the key nodes, wherein the sensor information includes: sensor type and sensor specification; A network is established based on the sensor information corresponding to each of the key nodes to obtain a sensor network.

[0013] In a second aspect, the present application provides a method for online monitoring of oil and gas recovery at a gas station, which adopts the following technical solution: Collecting the working parameters at each key node in the oil and gas recovery process, wherein the key node is the equipment part that affects the safety of oil and gas recovery; Performing preliminary data processing based on the working parameters to obtain oil and gas recovery performance parameters and refueling equipment warning information, wherein a plurality of refueling equipment are provided in a gas station; A multi-dimensional data analysis is performed based on each of the oil and gas recovery performance parameters to obtain a multi-dimensional monitoring result of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working state optimization analysis.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned gas station oil vapor recovery online monitoring method.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the above-mentioned online monitoring method for oil and gas recovery in a gas station.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The online monitoring system for oil and gas recovery in gas stations includes: a sensor network, an edge controller and a cloud computing monitoring platform. The sensor network is used to collect the working parameters of each key node in the oil and gas recovery process. The edge controller is used to obtain the working parameters corresponding to the refueling equipment from the sensor network in real time, and perform preliminary data processing based on the working parameters to obtain the oil and gas recovery performance parameters and the early warning information of the refueling equipment. The data calculation work is distributed on the edge controller and the cloud computing monitoring platform. The edge controller can perform preliminary data processing operations so that it can respond quickly when there is an abnormality in the oil and gas recovery process, reduce the computing burden of the cloud computing monitoring platform, and reduce the transmission delay of the processing data. Furthermore, the cloud computing monitoring platform is used to obtain the oil and gas recovery performance parameters sent by each edge controller in the gas station, and perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain the multi-dimensional monitoring results of oil and gas recovery. Performing multi-dimensional data analysis can timely discover and feedback abnormal situations in the oil and gas recovery process, ensure the efficient progress of the oil and gas recovery work, and at the same time, it can identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process.

[0017] Obtain the gas station deployment information and sensor network deployment information, build a three-dimensional model based on the gas station deployment information and sensor network deployment information, and obtain the oil and gas recovery three-dimensional model. Then, bind the oil and gas recovery performance parameters corresponding to each refueling equipment with the corresponding elements in the oil and gas recovery three-dimensional model, and use holographic projection technology to visualize the oil and gas recovery three-dimensional model after element binding to obtain a virtual three-dimensional image reflecting the real-time operation status of oil and gas recovery. The introduction of holographic projection technology not only provides an intuitive visual experience, but also provides strong support for decision-making, helping relevant technicians to promptly discover and deal with potential faults or performance problems, thereby improving the reliability and safety of the oil and gas recovery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural schematic diagram of an online monitoring system for oil and gas recovery in a gas station according to one embodiment of the present application; Figure 2 It is a flow chart of an online monitoring method for oil and gas recovery at a gas station according to one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides an online monitoring system for oil and gas recovery in a gas station, including: a sensor network 101, an edge controller 102 and a cloud computing monitoring platform 103, wherein: the sensor network 101 is used to collect working parameters at each key node in the oil and gas recovery process, wherein the sensor network 101 is constructed by the joint work of different types of sensors deployed at each key node, and the key node is a device part that affects the safety of oil and gas recovery; the edge controller 102 is used to perform preliminary data processing based on the working parameters to obtain oil and gas recovery performance parameters and refueling equipment early warning information, wherein a plurality of refueling equipment are arranged in the gas station, and each refueling equipment is provided with a corresponding edge controller 102; the cloud computing monitoring platform 103 is used to perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain a multi-dimensional monitoring result of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working status optimization analysis.

[0025] An online monitoring system for oil and gas recovery at a gas station, such as Figure 1As shown, the system includes a sensor network 101, an edge controller 102 and a cloud computing monitoring platform 103, wherein: The sensor network 101 is used to collect working parameters at key nodes in the oil and gas recovery process, wherein the sensor network 101 is constructed by the joint work of different types of sensors deployed at key nodes, and the key nodes are equipment parts that affect the safety of oil and gas recovery; For the embodiment of the present application, the sensor network 101 is constructed by the joint work of different types of sensors deployed at various key nodes in the oil and gas recovery process. The sensor types include but are not limited to: concentration sensors, pressure sensors, gas flow sensors, liquid flow sensors, temperature sensors, etc. The above different types of sensors should all have high precision, high stability and high sensitivity to ensure that the working parameters at different stages of the oil and gas recovery process can be accurately captured. Therefore, the online monitoring system for oil and gas recovery in gas stations uses the sensor network 101 to collect the working parameters at various key nodes in the gas recovery process. For example, a pressure sensor is used to detect the oil and gas pressure in pipelines, oil tank gas spaces and other parts during the oil and gas recovery process; a gas flow sensor is used to detect the return gas volume of the refueling gun; a liquid flow sensor is used to detect the volume of gasoline during refueling; and a temperature sensor is used to collect the oil and gas temperature. Of course, users can also set other types of sensors and collect other types of working parameters according to specific monitoring dimensions, and this embodiment of the present application is no longer limited. There are many ways to build the sensor network 101, which is not limited in the embodiments of the present application. In one feasible method, node screening is performed based on the oil and gas recovery process to determine multiple key nodes; sensor analysis is performed based on each key node to determine the sensor information corresponding to each key node, wherein the sensor information includes: sensor type and sensor specifications; network construction is performed based on the sensor information corresponding to each key node to obtain the sensor network 101.

[0026] The edge controller 102 is used to obtain the working parameters corresponding to the refueling equipment from the sensor network 101 in real time, perform preliminary data processing based on the working parameters, and obtain oil and gas recovery performance parameters and refueling equipment warning information. There are multiple refueling equipment in the gas station, and each refueling equipment is provided with a corresponding edge controller 102; For the embodiment of the present application, since there are multiple refueling equipment in the gas station, if the working parameters corresponding to all the refueling equipment in the gas station are placed in a processing center for data calculation, there will be problems such as data transmission delay, slow response speed and large amount of calculation, which will affect the accuracy of online monitoring of oil and gas recovery in the gas station to a certain extent, so that the oil and gas recovery process has stability and reliability problems. Therefore, in the embodiment of the present application, a distributed computing method is adopted, that is, the data calculation work is dispersed on the edge controller 102 and the cloud computing monitoring platform 103, and an edge controller 102 is set in each refueling equipment. The edge controller 102 can perform preliminary data processing operations so that it can respond quickly when there is an abnormality in the oil and gas recovery process, reducing the computing burden of the cloud computing monitoring platform 103 and reducing the transmission delay of processing data.

[0027] Specifically, the edge controller 102 obtains the working parameters corresponding to the refueling equipment from the sensor network 101 in real time, and performs preliminary data processing based on the working parameters to obtain the oil and gas recovery performance parameters, wherein the oil and gas recovery performance parameters include but are not limited to: liquid resistance, tightness, gas-liquid ratio, etc. Liquid resistance refers to the resistance of condensed liquid to pass through the pipeline due to other reasons caused by gas condensation liquid being retained in the oil and gas pipeline; tightness refers to the degree of tightness of the oil and gas recovery system under a certain gas pressure state; gas-liquid ratio refers to the ratio of the volume of oil and gas collected during refueling to the volume of gasoline added to the tank at the same time. For liquid resistance, the flow rate and pressure changes in the pipeline are measured by a flow sensor and a pressure sensor, and the pipeline liquid resistance is calculated using the principle of fluid mechanics; for tightness, the pressure changes inside the oil and gas recovery system are measured by a pressure sensor; for gas-liquid ratio, the ratio between the gas return volume detected by the gas flow sensor and the volume of gasoline detected by the liquid flow sensor during refueling is calculated to obtain the gas-liquid ratio. Of course, the oil and gas recovery performance parameters may also include: oil and gas concentration, oil and gas temperature, etc., which are no longer limited in the embodiments of the present application.

[0028] At the same time, the edge controller 102 also stores the warning requirements corresponding to each oil and gas recovery performance parameter. The edge controller 102 will perform warning matching based on the oil and gas recovery performance parameters and the warning requirements. When the oil and gas recovery performance parameters meet the warning requirements, the corresponding refueling equipment warning information is generated. The warning requirements are based on relevant national standards and industry standards, and are warning standards set for the oil and gas recovery process of gas stations to ensure the safe operation of gas stations. For example, the reasonable range of the gas-liquid ratio is set to 1.0 to 1.2, and a warning operation will be triggered if it exceeds this range.

[0029] The cloud computing monitoring platform 103 is used to obtain the oil and gas recovery performance parameters sent by each edge controller 102 in the gas station, and perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain multi-dimensional monitoring results of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working status optimization analysis.

[0030] For the embodiment of the present application, the cloud computing monitoring platform 103 is connected to each edge controller 102 via wireless communication, which is mainly responsible for big data processing and long-term data storage, and realizes multi-dimensional monitoring of oil and gas recovery of all refueling equipment in the gas station, so as to accurately identify defects, potential faults and optimizable operations in the oil and gas recovery process, which helps to improve the stability and reliability of the oil and gas recovery process.

[0031] Specifically, the oil and gas recovery performance parameters sent by each edge controller 102 in the gas station are obtained, and multi-dimensional data analysis is performed based on each oil and gas recovery performance parameter to obtain multi-dimensional monitoring results of oil and gas recovery. The multi-dimensional data analysis includes: fault prediction analysis and working state optimization analysis. Performing multi-dimensional data analysis can timely discover and feedback abnormal situations in the oil and gas recovery process, ensure the efficient progress of oil and gas recovery work, and at the same time, identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process. There are many ways to implement multi-dimensional data analysis, which are no longer limited in the embodiments of the present application. In one feasible method, data classification is performed based on each oil and gas recovery performance parameter to obtain a multi-dimensional performance parameter set; a working status analysis is performed based on each performance parameter set to determine the working status analysis result, wherein the working status analysis result includes: normal working status and abnormal working status; when the working status analysis result is that the working status is normal, a potential fault prediction is performed based on each performance parameter set to determine the potential fault prediction result, and a working optimization analysis is performed based on each performance parameter set to determine the working optimization analysis result; the working status analysis result, the potential fault prediction result and the working optimization analysis result are combined to obtain a multi-dimensional monitoring result of oil and gas recovery.

[0032] It can be seen that in the embodiment of the present application, the online monitoring system for oil and gas recovery in a gas station includes: a sensor network 101, an edge controller 102 and a cloud computing monitoring platform 103. The sensor network 101 is used to collect the working parameters at each key node in the oil and gas recovery process. The edge controller 102 is used to obtain the working parameters corresponding to the refueling equipment from the sensor network 101 in real time, and perform preliminary data processing based on the working parameters to obtain oil and gas recovery performance parameters and refueling equipment warning information. The data calculation work is distributed on the edge controller 102 and the cloud computing monitoring platform 103. The edge controller 102 can perform preliminary data processing operations so that it can respond quickly when there is an abnormality in the oil and gas recovery process, which reduces the computing burden of the cloud computing monitoring platform 103 and reduces the transmission delay of the processing data. Furthermore, the cloud computing monitoring platform 103 is used to obtain the oil and gas recovery performance parameters sent by each edge controller 102 in the gas station, and perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain the multi-dimensional monitoring results of oil and gas recovery. Performing multi-dimensional data analysis can timely discover and feedback abnormal situations in the oil and gas recovery process, ensuring the efficient implementation of the oil and gas recovery work. At the same time, it can identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process.

[0033] Furthermore, in order to reduce downtime and maintenance costs caused by failures and improve the stability and reliability of the oil and gas recovery process, in the embodiment of the present application, the cloud computing monitoring platform 103 performs multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain a multi-dimensional monitoring result of oil and gas recovery, and is used to: Data is classified based on each oil and gas recovery performance parameter to obtain a multi-dimensional performance parameter set; Performing a working state analysis based on each performance parameter set to determine a working state analysis result, wherein the working state analysis result includes: a normal working state and an abnormal working state; When the working state analysis result is that the working state is normal, a potential fault prediction is performed based on each performance parameter set to determine the potential fault prediction result, and a working optimization analysis is performed based on each performance parameter set to determine the working optimization analysis result; The multi-dimensional monitoring results of oil and gas recovery are obtained by integrating the working status analysis results, potential fault prediction results and working optimization analysis results.

[0034] For the embodiment of the present application, a plurality of refueling equipment are provided in the gas station, and the oil and gas recovery performance parameters calculated by the edge controller 102 also include multiple dimensions. In order to facilitate the subsequent analysis of the single-dimensional data of a single refueling equipment, data classification is performed based on each oil and gas recovery performance parameter to obtain a multi-dimensional performance parameter set, wherein each performance parameter set refers to a set of oil and gas recovery performance parameters of the same refueling equipment under the same data dimension at different times, and the oil and gas recovery performance parameters in the performance parameter set are sorted according to time series, for example, the performance parameter set is a gas-liquid ratio parameter set of refueling equipment A. The cloud computing monitoring platform 103 pre-stores the normal working data range of each dimensional performance parameter, and performs data matching based on each performance parameter set and the corresponding normal working data range. If they are all within the normal working data range, the working status analysis result is determined to be normal; otherwise, the working status analysis result is determined to be abnormal.

[0035] When the working state analysis result is that the working state is normal, it indicates that the oil and gas recovery process of the refueling equipment does not directly show an abnormal state, but it does not mean that the oil and gas recovery process has no abnormal performance. Therefore, a potential fault prediction is performed based on each performance parameter set to determine the potential fault prediction result. The potential fault prediction aims to identify early signs that may cause abnormal oil and gas recovery in the future by analyzing the performance parameter set of the current normal operation, which helps to take preventive measures before the actual failure occurs, and avoid or reduce the downtime and maintenance costs of the refueling equipment. There are many ways to implement potential fault prediction, and the embodiments of the present application are no longer limited. In one achievable method, a fault prediction model is obtained. The fault prediction model is obtained by training a machine learning algorithm using a large amount of historical data. The historical data is composed of early performance parameters of refueling equipment that has already failed. Then, the performance parameter set is predicted using the fault prediction model to determine the potential fault prediction result, wherein the potential fault prediction result includes: whether there is a potential fault, the type of potential fault, the type of potential fault, and other information.

[0036] At the same time, as the oil and gas recovery time increases, bottlenecks and inefficient environments may occur. In order to improve the oil and gas recovery efficiency, work optimization analysis is performed based on each performance parameter set to determine the work optimization analysis results, so that when the oil and gas recovery work is no longer in the optimal state, the work flow can be adjusted in time to improve the oil and gas recovery efficiency and ensure the safe and stable operation of the oil and gas recovery process. The cloud computing monitoring platform 103 pre-sets the optimal value corresponding to each dimensional performance parameter. If the deviation between the average value of the performance parameter set and the optimal value is greater than the preset deviation threshold, the work optimization analysis result is determined to be to be optimized; otherwise, the work optimization analysis result is determined to be good, wherein the threshold of the preset deviation threshold is determined by relevant technicians through a large number of experimental calculations, and the embodiment of the present application does not limit the specific value. Finally, the multi-dimensional monitoring results of oil and gas recovery are obtained by combining the work status analysis results, potential fault prediction results and work optimization analysis results. Performing multi-dimensional data analysis can timely discover and feedback abnormal conditions in the oil and gas recovery process, ensure the efficient progress of the oil and gas recovery work, and at the same time, it can identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process.

[0037] It can be seen that in the embodiment of the present application, data classification is performed based on each oil and gas recovery performance parameter to obtain a multi-dimensional performance parameter set, and then a working state analysis is performed based on each performance parameter set to determine the working state analysis result. When the working state analysis result is that the working state is normal, a potential fault prediction is performed based on each performance parameter set to determine the potential fault prediction result, and a work optimization analysis is performed based on each performance parameter set to determine the work optimization analysis result. Finally, the working state analysis results, potential fault prediction results and work optimization analysis results are combined to obtain a multi-dimensional monitoring result of oil and gas recovery. Performing multi-dimensional data analysis can timely discover and feedback abnormal situations in the oil and gas recovery process, ensure the efficient progress of oil and gas recovery work, and at the same time, can identify potential fault points in advance, reduce downtime and maintenance costs caused by faults, and improve the stability and reliability of the oil and gas recovery process.

[0038] Furthermore, in order to ensure the continuous and effective operation of the oil and gas recovery system, thereby reducing the negative impact on the environment, in the embodiment of the present application, the cloud computing monitoring platform 103 is also used to: When the working state analysis result is that the working state is abnormal, abnormal data extraction is performed based on each performance parameter set to obtain abnormal performance parameters; Abnormal performance analysis is performed based on abnormal performance parameters to obtain abnormal performance characteristics, and based on the relationship between abnormal performance characteristics and operation and maintenance processing, the fault suggestion processing plan corresponding to the abnormal performance characteristics is determined. Among them, the fault suggestion processing plan provides fault processing suggestions for gas station managers to quickly resolve abnormal working conditions.

[0039] For the embodiment of the present application, when the working state analysis result is that the working state is abnormal, abnormal data extraction is performed based on each performance parameter set to obtain abnormal performance parameters, which are performance parameter sets that are not within the normal working data range. Then, abnormal performance analysis is performed based on the abnormal performance parameters to obtain abnormal performance characteristics, which include but are not limited to: abnormal type, abnormal level, etc.

[0040] Specifically, since the abnormal performance parameter corresponds to at least one data detection dimension of the refueling equipment, that is, the abnormal performance parameter is a performance parameter of different dimensions with an abnormality, when performing abnormal performance analysis on the abnormal performance parameter based on the AI ​​algorithm, preferably, the abnormal performance parameter is subjected to data association operation to obtain abnormal performance parameter association data, wherein the abnormal performance parameter association data is a knowledge graph including the equipment entity, fault attribute and equipment entity relationship corresponding to the performance parameter, and the data association operation can establish a close connection between the scattered abnormal performance parameters of different dimensions. At the same time, the AI ​​algorithm is a fault knowledge system, which is a fault corpus formed based on a large amount of preset fault data, and there is an abnormal performance feature corresponding to each preset fault association data. Then, based on the relationship between the abnormal performance feature and the operation and maintenance processing, the fault suggestion processing scheme corresponding to the abnormal performance feature is determined, wherein the fault suggestion processing scheme includes but is not limited to: replacing damaged parts, adjusting equipment parameters or performing repairs and maintenance, and the fault suggestion processing scheme provides fault processing suggestions for gas station managers, so as to quickly resolve abnormal working conditions and ensure the continuous and effective operation of the oil and gas recovery system, thereby reducing the negative impact on the environment.

[0041] It can be seen that in the embodiment of the present application, when the working state analysis result is that the working state is abnormal, abnormal data extraction is performed based on each performance parameter set to obtain abnormal performance parameters. Then, abnormal performance analysis is performed based on the abnormal performance parameters to obtain abnormal performance characteristics, and based on the relationship between the abnormal performance characteristics and operation and maintenance processing, the fault suggestion processing plan corresponding to the abnormal performance characteristics is determined. The fault suggestion processing plan provides fault handling suggestions for gas station managers to quickly resolve abnormal working conditions and ensure the continued effective operation of the oil and gas recovery system, thereby reducing negative impacts on the environment.

[0042] Furthermore, in order to improve the reliability and safety of the oil and gas recovery process, in the embodiment of the present application, it also includes: The holographic projection display platform is used to obtain the gas station deployment information and the sensor network 101 deployment information, and to construct a three-dimensional model based on the gas station deployment information and the sensor network 101 deployment information to obtain an oil and gas recovery three-dimensional model; Based on the binding of the oil and gas recovery performance parameters corresponding to each refueling equipment with the corresponding elements in the oil and gas recovery three-dimensional model, the holographic projection technology is used to visualize the oil and gas recovery three-dimensional model after element binding to obtain a virtual three-dimensional image reflecting the real-time operating status of oil and gas recovery.

[0043] For the embodiment of the present application, the gas station deployment information is obtained. The gas station deployment information can be obtained through field investigations, design drawings or existing databases. The gas station deployment information includes but is not limited to: the location, quantity, type of refueling equipment, and the location and specifications of other key equipment in the gas station. The gas station deployment information also records the model, performance parameters and connection method of each device. At the same time, the sensor network 101 deployment information is used to characterize the deployment location, data volume and type of each sensor in the sensor network. Of course, it also includes detailed information such as the connection method between sensors and the data transmission protocol. Then, based on the gas station deployment information, the initial model is constructed to build a basic model of the gas station. Then, based on the basic model, the sensor model is added according to the sensor network 101 deployment information. The above sensor model should be able to accurately reflect the location and connection method of the actual sensor. Finally, a three-dimensional model of oil and gas recovery is obtained.

[0044] Then, in the three-dimensional model of oil and gas recovery, elements related to the oil and gas recovery performance parameters corresponding to each refueling equipment are identified and determined. These elements may be a part or component in the three-dimensional model of oil and gas recovery, such as a recovery device, a pipeline, etc. Then, the collected oil and gas recovery performance parameters corresponding to each refueling equipment are bound to the corresponding elements in the three-dimensional model of oil and gas recovery to establish a data mapping relationship, that is, the parameter value is associated with the element in the model so that it can be updated and displayed in real time in subsequent steps. Then, the three-dimensional model of oil and gas recovery after element binding is loaded into the holographic projection system, and the three-dimensional model of oil and gas recovery after element binding is visualized and rendered using holographic projection technology to obtain a virtual three-dimensional image reflecting the real-time operating status of oil and gas recovery. The introduction of holographic projection technology to create a three-dimensional virtual model to display the operating status of the oil and gas recovery system of a gas station not only provides an intuitive visual experience, but also provides strong support for decision-making, which helps relevant technicians to timely discover and deal with potential faults or performance problems, thereby improving the reliability and safety of the oil and gas recovery process.

[0045] It can be seen that in the embodiment of the present application, the gas station deployment information and the sensor network 101 deployment information are obtained, and a three-dimensional model is constructed based on the gas station deployment information and the sensor network 101 deployment information to obtain an oil and gas recovery three-dimensional model. Then, the oil and gas recovery performance parameters corresponding to each refueling equipment are bound to the corresponding elements in the oil and gas recovery three-dimensional model, and the holographic projection technology is used to visualize the oil and gas recovery three-dimensional model after the elements are bound, so as to obtain a virtual three-dimensional image reflecting the real-time operating status of the oil and gas recovery. The introduction of holographic projection technology not only provides an intuitive visual experience, but also provides strong support for decision-making, which helps relevant technical personnel to promptly discover and deal with potential faults or performance problems, thereby improving the reliability and safety of the oil and gas recovery process.

[0046] Furthermore, in order to effectively prevent the expansion of faults or the occurrence of safety accidents, in the embodiment of the present application, the cloud computing monitoring platform 103 is also used to: Acquire the refueling equipment warning information sent by each edge controller 102 in real time, and count the number of warnings based on the refueling equipment warning information to determine the number of warnings; Obtain alarm monitoring requirements, perform alarm analysis based on the number of warnings, fueling equipment warning information and alarm monitoring requirements, and determine the alarm analysis results and alarm control methods; When the alarm analysis result is to execute the alarm, the refueling equipment corresponding to the edge controller 102 is controlled according to the alarm control method, and the alarm device is controlled to alarm.

[0047] For the embodiment of the present application, the edge controller 102 can respond quickly and issue an early warning when an abnormality occurs in the oil and gas recovery process. However, for refueling equipment that frequently issues early warnings, alarm control needs to be performed to ensure the safe operation of each refueling equipment in the gas station. Therefore, the early warning information of the refueling equipment sent by each edge controller 102 is obtained in real time, and the number of early warnings is counted based on the early warning information of the refueling equipment to determine the number of early warnings, wherein the number of early warnings refers to the number of times the same refueling equipment issues early warnings within a preset period of time, and the preset period of time can be one day, one week, one month, etc. The length of the preset period of time is no longer limited in the embodiment of the present application. At the same time, the cloud computing monitoring platform 103 is set with an alarm monitoring requirement, and the alarm monitoring requirement records the conditions under which different types of early warning information are executed to perform alarm control. Therefore, an alarm analysis is performed based on the number of early warnings, the early warning information of the refueling equipment, and the alarm monitoring requirements, and the alarm analysis results and the alarm control method are determined, that is, when the early warning information and the number of early warnings of the refueling equipment reach the alarm standard of the alarm monitoring requirements, the alarm analysis result is determined to be an execution alarm, and the corresponding alarm control method is selected. Furthermore, when the alarm analysis result is to execute an alarm, the refueling equipment corresponding to the edge controller 102 is controlled according to the alarm control method, and the alarm device is controlled to alarm. When the alarm analysis result is not to alarm, no additional operation is performed, and the refueling equipment warning information sent by the edge controller 102 can be continuously obtained. When the alarm analysis result is to execute an alarm, the cloud computing monitoring platform 103 can automatically control the refueling equipment corresponding to the edge controller 102 according to the alarm control method, effectively preventing the expansion of faults or the occurrence of safety accidents.

[0048] The specific content of the alarm monitoring requirements is based on national standards and is no longer limited in the embodiments of the present application. Users can adjust them according to actual conditions. For example, when it is detected that the refueling equipment is in a warning state for seven consecutive days, the alarm analysis result is determined to be an execution alarm, and the abnormal refueling equipment is locked.

[0049] It can be seen that in the embodiment of the present application, the refueling equipment warning information sent by each edge controller 102 is obtained in real time, and the number of warnings is counted based on the refueling equipment warning information to determine the number of warnings. Then, based on the number of warnings, the refueling equipment warning information and the alarm monitoring requirements, an alarm analysis is performed to determine the alarm analysis results and the alarm control method. Furthermore, when the alarm analysis result is to execute an alarm, the refueling equipment corresponding to the edge controller 102 is controlled according to the alarm control method, and the alarm device is controlled to alarm, effectively preventing the expansion of the fault or the occurrence of a safety accident.

[0050] Furthermore, in order to improve the accuracy and comprehensiveness of monitoring the oil and gas recovery process, in the embodiment of the present application, it also includes: The sensor network 101 builds a module for node screening based on the oil and gas recovery process and determines multiple key nodes; Perform sensor analysis based on each key node to determine sensor information corresponding to each key node, wherein the sensor information includes: sensor type and sensor specification; The network is constructed based on the sensor information corresponding to each key node to obtain the sensor network 101 .

[0051] For the embodiments of the present application, in order to improve the accuracy and comprehensiveness of monitoring the oil and gas recovery process, different types of sensors are set at important links and important equipment in the oil and gas recovery process, so as to obtain multi-dimensional working parameters in the oil and gas recovery process in real time through sensors. Therefore, node screening is performed based on the oil and gas recovery process to determine multiple key nodes, which are links that have a greater impact on oil and gas recovery performance, a higher risk of environmental pollution, or a higher probability of failure.

[0052] Then, sensor analysis is performed based on each key node to determine the sensor information corresponding to each key node, wherein the sensor information includes: sensor type and sensor specification. According to the specific environment and requirements of each key node, the corresponding sensor type and specification are selected to ensure that the sensor can accurately and efficiently collect working parameters. In the process of performing sensor analysis, it is preferred to determine which dimensions of data need to be collected at each key node to select the sensor type corresponding to the dimension. For example, for the oil unloading oil and gas recovery port, manhole cover, and refueling machine, it is necessary to focus on monitoring whether there is oil and gas leakage. Therefore, concentration sensors are installed at the above key nodes to monitor the oil and gas concentration; for the gas path in the refueling machine, it is necessary to monitor the return gas volume of each refueling operation, so gas flow sensors are installed at the above key nodes; for the oil pipeline of the refueling machine, it is necessary to monitor the flow of oil during each refueling process, so liquid flow sensors are installed at the above key nodes. Finally, a network is established based on the sensor information corresponding to each key node to obtain a sensor network 101, wherein the sensor network 101 can flexibly adapt to different monitoring needs and can be expanded or adjusted according to actual conditions. That is, the sensor information is classified and encoded first to facilitate subsequent data processing and network management. Then, according to the monitoring needs and sensor distribution, a suitable network topology is selected, such as star, tree, mesh, etc., where the network topology can cover all key nodes and realize the effective transmission and processing of data; then, according to the sensor information and network topology corresponding to each key node, the network nodes are configured to ensure that each node can correctly identify and connect to its adjacent nodes and realize the normal transmission of data.

[0053] It can be seen that in the embodiment of the present application, in order to improve the accuracy and comprehensiveness of monitoring the oil and gas recovery process, node screening is performed based on the oil and gas recovery process to determine multiple key nodes, so as to obtain multi-dimensional working parameters in the oil and gas recovery process in real time through sensors. Then, sensor analysis is performed based on each key node to determine the sensor information corresponding to each key node. The sensor information is used to ensure that the sensor can accurately and efficiently collect working parameters. Finally, a network is established based on the sensor information corresponding to each key node to obtain a sensor network 101.

[0054] The above embodiment introduces an online monitoring system for oil and gas recovery at a gas station from the perspective of a system. The following embodiment introduces an online monitoring method for oil and gas recovery at a gas station from the perspective of a method flow, including step S201, step S202, and step S203, wherein: Step S201: collecting working parameters at key nodes in the oil and gas recovery process, wherein the key nodes are equipment parts that affect the safety of oil and gas recovery; Step S202: Perform preliminary data processing based on the working parameters to obtain oil and gas recovery performance parameters and refueling equipment warning information, wherein a plurality of refueling equipment are provided in the gas station; Step S203: Perform multi-dimensional data analysis based on each oil and gas recovery performance parameter to obtain a multi-dimensional monitoring result of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working state optimization analysis.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described method for online monitoring of oil and gas recovery at a gas station can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0056] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0057] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0058] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0059] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0060] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0061] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0063] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0064] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0065] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An online monitoring system for oil and gas recovery at a gas station, characterized in that: include: Sensor networks, edge controllers, and cloud computing monitoring platforms, including: A sensor network for collecting working parameters at key nodes in the oil and gas recovery process, wherein the sensor network is constructed by the joint work of different types of sensors deployed at key nodes, and the key nodes are equipment parts that affect the safety of oil and gas recovery; The edge controller is used to obtain the working parameters corresponding to the refueling equipment from the sensor network in real time, perform preliminary data processing based on the working parameters, and obtain oil and gas recovery performance parameters and refueling equipment warning information, wherein a plurality of the refueling equipment are provided in the gas station, and each of the refueling equipment is provided with a corresponding edge controller; The cloud computing monitoring platform is used to obtain the oil and gas recovery performance parameters sent by each edge controller in the gas station, perform multi-dimensional data analysis based on each oil and gas recovery performance parameter, and obtain multi-dimensional monitoring results of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working status optimization analysis.

2. The online monitoring system for oil and gas recovery at a gas station according to claim 1, characterized in that: When the cloud computing monitoring platform performs the multi-dimensional data analysis based on each of the oil and gas recovery performance parameters to obtain the oil and gas recovery multi-dimensional monitoring results, it is used to: Performing data classification based on each of the oil and gas recovery performance parameters to obtain a multi-dimensional performance parameter set; Performing a working state analysis based on each of the performance parameter sets to determine a working state analysis result, wherein the working state analysis result includes: a normal working state and an abnormal working state; When the working state analysis result is that the working state is normal, a potential fault prediction is performed based on each of the performance parameter sets to determine a potential fault prediction result, and a working optimization analysis is performed based on each of the performance parameter sets to determine a working optimization analysis result; The multi-dimensional monitoring result of oil and gas recovery is obtained by combining the working status analysis result, the potential fault prediction result and the working optimization analysis result.

3. The online monitoring system for oil and gas recovery at a gas station according to claim 2 is characterized in that: The cloud computing monitoring platform is also used for: When the working state analysis result is that the working state is abnormal, abnormal data extraction is performed based on each of the performance parameter sets to obtain abnormal performance parameters; An abnormal performance analysis is performed based on the abnormal performance parameters to obtain abnormal performance characteristics, and based on the relationship between the abnormal performance characteristics and operation and maintenance processing, a fault suggestion processing plan corresponding to the abnormal performance characteristics is determined, wherein the fault suggestion processing plan provides fault processing suggestions for gas station managers to facilitate rapid resolution of abnormal working conditions.

4. The online monitoring system for oil and gas recovery at a gas station according to claim 1, characterized in that: Also includes: A holographic projection display platform is used to obtain gas station deployment information and sensor network deployment information, and to construct a three-dimensional model based on the gas station deployment information and the sensor network deployment information to obtain an oil and gas recovery three-dimensional model; Based on the oil and gas recovery performance parameters corresponding to each refueling equipment and the corresponding elements in the oil and gas recovery three-dimensional model, the oil and gas recovery three-dimensional model after element binding is visualized and rendered using holographic projection technology to obtain a virtual three-dimensional image reflecting the real-time operating status of oil and gas recovery.

5. The online monitoring system for oil and gas recovery at a gas station according to claim 1, characterized in that: The cloud computing monitoring platform is also used for: Acquire the refueling equipment warning information sent by each edge controller in real time, and count the number of warnings based on the refueling equipment warning information to determine the number of warnings; Acquire alarm monitoring requirements, perform alarm analysis based on the number of warnings, the refueling equipment warning information and the alarm monitoring requirements, and determine alarm analysis results and alarm control methods; When the alarm analysis result is to execute an alarm, the refueling equipment corresponding to the edge controller is controlled according to the alarm control method, and the alarm device is controlled to alarm.

6. The online monitoring system for oil and gas recovery at a gas station according to claim 1, characterized in that: Also includes: A sensor network building module, used for screening nodes based on the oil and gas recovery process and determining multiple key nodes; Performing sensor analysis based on each of the key nodes to determine sensor information corresponding to each of the key nodes, wherein the sensor information includes: sensor type and sensor specification; A network is established based on the sensor information corresponding to each of the key nodes to obtain a sensor network.

7. A method for online monitoring of oil and gas recovery at a gas station, characterized in that: include: Collecting the working parameters at each key node in the oil and gas recovery process, wherein the key node is the equipment part that affects the safety of oil and gas recovery; Performing preliminary data processing based on the working parameters to obtain oil and gas recovery performance parameters and refueling equipment warning information, wherein a plurality of refueling equipment are provided in a gas station; A multi-dimensional data analysis is performed based on each of the oil and gas recovery performance parameters to obtain a multi-dimensional monitoring result of oil and gas recovery, wherein the multi-dimensional data analysis includes: fault prediction analysis and working state optimization analysis.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the online monitoring method for oil vapor recovery in a gas station as described in claim 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the online monitoring method for oil and gas recovery in a gas station as described in claim 7.

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